Overview

Dataset statistics

Number of variables23
Number of observations4062
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory733.9 KiB
Average record size in memory185.0 B

Variable types

Numeric9
Categorical13
Boolean1

Alerts

vein_type has constant value ""Constant
cap_color is highly overall correlated with stalk_shape and 1 other fieldsHigh correlation
odor is highly overall correlated with bruises and 7 other fieldsHigh correlation
gill_color is highly overall correlated with bruises and 5 other fieldsHigh correlation
stalk_color_above_ring is highly overall correlated with stalk_color_below_ring and 6 other fieldsHigh correlation
stalk_color_below_ring is highly overall correlated with stalk_color_above_ring and 6 other fieldsHigh correlation
spore_print_color is highly overall correlated with bruises and 6 other fieldsHigh correlation
population is highly overall correlated with gill_spacing and 2 other fieldsHigh correlation
habitat is highly overall correlated with bruises and 2 other fieldsHigh correlation
bruises is highly overall correlated with odor and 8 other fieldsHigh correlation
gill_attachment is highly overall correlated with gill_color and 4 other fieldsHigh correlation
gill_spacing is highly overall correlated with population and 2 other fieldsHigh correlation
gill_size is highly overall correlated with odor and 6 other fieldsHigh correlation
stalk_shape is highly overall correlated with cap_color and 5 other fieldsHigh correlation
stalk_root is highly overall correlated with odor and 8 other fieldsHigh correlation
stalk_surface_above_ring is highly overall correlated with bruises and 1 other fieldsHigh correlation
stalk_surface_below_ring is highly overall correlated with odor and 4 other fieldsHigh correlation
vein_color is highly overall correlated with stalk_color_above_ring and 3 other fieldsHigh correlation
ring_number is highly overall correlated with odor and 3 other fieldsHigh correlation
ring_type is highly overall correlated with cap_color and 10 other fieldsHigh correlation
edible is highly overall correlated with odor and 9 other fieldsHigh correlation
gill_attachment is highly imbalanced (82.7%)Imbalance
vein_color is highly imbalanced (90.4%)Imbalance
ring_number is highly imbalanced (73.8%)Imbalance
cap_shape has 226 (5.6%) zerosZeros
cap_color has 80 (2.0%) zerosZeros
odor has 189 (4.7%) zerosZeros
gill_color has 905 (22.3%) zerosZeros
stalk_color_above_ring has 216 (5.3%) zerosZeros
stalk_color_below_ring has 223 (5.5%) zerosZeros
population has 193 (4.8%) zerosZeros
habitat has 1566 (38.6%) zerosZeros

Reproduction

Analysis started2023-03-04 22:07:20.777694
Analysis finished2023-03-04 22:07:31.669826
Duration10.89 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

cap_shape
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3362875
Minimum0
Maximum5
Zeros226
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:31.722294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5997284
Coefficient of variation (CV)0.47949357
Kurtosis-1.2362541
Mean3.3362875
Median Absolute Deviation (MAD)1.5
Skewness-0.23344588
Sum13552
Variance2.5591311
MonotonicityNot monotonic
2023-03-04T22:07:31.800287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 1804
44.4%
2 1580
38.9%
3 433
 
10.7%
0 226
 
5.6%
4 18
 
0.4%
1 1
 
< 0.1%
ValueCountFrequency (%)
0 226
 
5.6%
1 1
 
< 0.1%
2 1580
38.9%
3 433
 
10.7%
4 18
 
0.4%
5 1804
44.4%
ValueCountFrequency (%)
5 1804
44.4%
4 18
 
0.4%
3 433
 
10.7%
2 1580
38.9%
1 1
 
< 0.1%
0 226
 
5.6%

cap_surface
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
3.0
1611 
2.0
1289 
0.0
1160 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
3.0 1611
39.7%
2.0 1289
31.7%
0.0 1160
28.6%
1.0 2
 
< 0.1%

Length

2023-03-04T22:07:31.882621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:31.976848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1611
39.7%
2.0 1289
31.7%
0.0 1160
28.6%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5222
42.9%
. 4062
33.3%
3 1611
 
13.2%
2 1289
 
10.6%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5222
64.3%
3 1611
 
19.8%
2 1289
 
15.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5222
42.9%
. 4062
33.3%
3 1611
 
13.2%
2 1289
 
10.6%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5222
42.9%
. 4062
33.3%
3 1611
 
13.2%
2 1289
 
10.6%
1 2
 
< 0.1%

cap_color
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5189562
Minimum0
Maximum9
Zeros80
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:32.056493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q38
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5454277
Coefficient of variation (CV)0.5632778
Kurtosis-0.86300411
Mean4.5189562
Median Absolute Deviation (MAD)1
Skewness0.69608919
Sum18356
Variance6.4792023
MonotonicityNot monotonic
2023-03-04T22:07:32.128551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 1136
28.0%
3 907
22.3%
2 758
18.7%
8 537
13.2%
9 528
13.0%
0 80
 
2.0%
5 73
 
1.8%
1 21
 
0.5%
6 13
 
0.3%
7 9
 
0.2%
ValueCountFrequency (%)
0 80
 
2.0%
1 21
 
0.5%
2 758
18.7%
3 907
22.3%
4 1136
28.0%
5 73
 
1.8%
6 13
 
0.3%
7 9
 
0.2%
8 537
13.2%
9 528
13.0%
ValueCountFrequency (%)
9 528
13.0%
8 537
13.2%
7 9
 
0.2%
6 13
 
0.3%
5 73
 
1.8%
4 1136
28.0%
3 907
22.3%
2 758
18.7%
1 21
 
0.5%
0 80
 
2.0%

bruises
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
0.0
2393 
1.0
1669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 2393
58.9%
1.0 1669
41.1%

Length

2023-03-04T22:07:32.209260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:32.295068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2393
58.9%
1.0 1669
41.1%

Most occurring characters

ValueCountFrequency (%)
0 6455
53.0%
. 4062
33.3%
1 1669
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6455
79.5%
1 1669
 
20.5%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6455
53.0%
. 4062
33.3%
1 1669
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6455
53.0%
. 4062
33.3%
1 1669
 
13.7%

odor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1779911
Minimum0
Maximum8
Zeros189
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:32.366438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q35
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1049953
Coefficient of variation (CV)0.50382953
Kurtosis-0.77530321
Mean4.1779911
Median Absolute Deviation (MAD)2
Skewness-0.071591131
Sum16971
Variance4.4310053
MonotonicityNot monotonic
2023-03-04T22:07:32.446200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 1753
43.2%
2 1068
26.3%
8 305
 
7.5%
7 295
 
7.3%
3 220
 
5.4%
0 189
 
4.7%
6 125
 
3.1%
1 91
 
2.2%
4 16
 
0.4%
ValueCountFrequency (%)
0 189
 
4.7%
1 91
 
2.2%
2 1068
26.3%
3 220
 
5.4%
4 16
 
0.4%
5 1753
43.2%
6 125
 
3.1%
7 295
 
7.3%
8 305
 
7.5%
ValueCountFrequency (%)
8 305
 
7.5%
7 295
 
7.3%
6 125
 
3.1%
5 1753
43.2%
4 16
 
0.4%
3 220
 
5.4%
2 1068
26.3%
1 91
 
2.2%
0 189
 
4.7%

gill_attachment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
1.0
3957 
0.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3957
97.4%
0.0 105
 
2.6%

Length

2023-03-04T22:07:32.536293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:32.622379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3957
97.4%
0.0 105
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 4167
34.2%
. 4062
33.3%
1 3957
32.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4167
51.3%
1 3957
48.7%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4167
34.2%
. 4062
33.3%
1 3957
32.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4167
34.2%
. 4062
33.3%
1 3957
32.5%

gill_spacing
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
0.0
3425 
1.0
637 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3425
84.3%
1.0 637
 
15.7%

Length

2023-03-04T22:07:32.694387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:32.785928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3425
84.3%
1.0 637
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 7487
61.4%
. 4062
33.3%
1 637
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7487
92.2%
1 637
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7487
61.4%
. 4062
33.3%
1 637
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7487
61.4%
. 4062
33.3%
1 637
 
5.2%

gill_size
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
0.0
2778 
1.0
1284 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2778
68.4%
1.0 1284
31.6%

Length

2023-03-04T22:07:32.861280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:32.949436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2778
68.4%
1.0 1284
31.6%

Most occurring characters

ValueCountFrequency (%)
0 6840
56.1%
. 4062
33.3%
1 1284
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6840
84.2%
1 1284
 
15.8%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6840
56.1%
. 4062
33.3%
1 1284
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6840
56.1%
. 4062
33.3%
1 1284
 
10.5%

gill_color
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7690793
Minimum0
Maximum11
Zeros905
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:33.023804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5568828
Coefficient of variation (CV)0.74582169
Kurtosis-1.2941287
Mean4.7690793
Median Absolute Deviation (MAD)3
Skewness0.066554117
Sum19372
Variance12.651415
MonotonicityNot monotonic
2023-03-04T22:07:33.104849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 905
22.3%
7 755
18.6%
10 593
14.6%
5 510
12.6%
2 361
 
8.9%
3 349
 
8.6%
9 237
 
5.8%
4 216
 
5.3%
1 45
 
1.1%
11 44
 
1.1%
Other values (2) 47
 
1.2%
ValueCountFrequency (%)
0 905
22.3%
1 45
 
1.1%
2 361
 
8.9%
3 349
 
8.6%
4 216
 
5.3%
5 510
12.6%
6 32
 
0.8%
7 755
18.6%
8 15
 
0.4%
9 237
 
5.8%
ValueCountFrequency (%)
11 44
 
1.1%
10 593
14.6%
9 237
 
5.8%
8 15
 
0.4%
7 755
18.6%
6 32
 
0.8%
5 510
12.6%
4 216
 
5.3%
3 349
8.6%
2 361
8.9%

stalk_shape
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
1.0
2314 
0.0
1748 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2314
57.0%
0.0 1748
43.0%

Length

2023-03-04T22:07:33.190651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:33.278000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2314
57.0%
0.0 1748
43.0%

Most occurring characters

ValueCountFrequency (%)
0 5810
47.7%
. 4062
33.3%
1 2314
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5810
71.5%
1 2314
 
28.5%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5810
47.7%
. 4062
33.3%
1 2314
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5810
47.7%
. 4062
33.3%
1 2314
 
19.0%

stalk_root
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
1.0
1832 
0.0
1284 
3.0
560 
2.0
284 
4.0
 
102

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1832
45.1%
0.0 1284
31.6%
3.0 560
 
13.8%
2.0 284
 
7.0%
4.0 102
 
2.5%

Length

2023-03-04T22:07:33.354920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:33.448395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1832
45.1%
0.0 1284
31.6%
3.0 560
 
13.8%
2.0 284
 
7.0%
4.0 102
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 5346
43.9%
. 4062
33.3%
1 1832
 
15.0%
3 560
 
4.6%
2 284
 
2.3%
4 102
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5346
65.8%
1 1832
 
22.6%
3 560
 
6.9%
2 284
 
3.5%
4 102
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5346
43.9%
. 4062
33.3%
1 1832
 
15.0%
3 560
 
4.6%
2 284
 
2.3%
4 102
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5346
43.9%
. 4062
33.3%
1 1832
 
15.0%
3 560
 
4.6%
2 284
 
2.3%
4 102
 
0.8%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
2.0
2607 
1.0
1173 
0.0
273 
3.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2607
64.2%
1.0 1173
28.9%
0.0 273
 
6.7%
3.0 9
 
0.2%

Length

2023-03-04T22:07:33.533134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:33.626228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2607
64.2%
1.0 1173
28.9%
0.0 273
 
6.7%
3.0 9
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4335
35.6%
. 4062
33.3%
2 2607
21.4%
1 1173
 
9.6%
3 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4335
53.4%
2 2607
32.1%
1 1173
 
14.4%
3 9
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4335
35.6%
. 4062
33.3%
2 2607
21.4%
1 1173
 
9.6%
3 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4335
35.6%
. 4062
33.3%
2 2607
21.4%
1 1173
 
9.6%
3 9
 
0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
2.0
2476 
1.0
1156 
0.0
288 
3.0
 
142

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2476
61.0%
1.0 1156
28.5%
0.0 288
 
7.1%
3.0 142
 
3.5%

Length

2023-03-04T22:07:33.707740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:33.798412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2476
61.0%
1.0 1156
28.5%
0.0 288
 
7.1%
3.0 142
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 4350
35.7%
. 4062
33.3%
2 2476
20.3%
1 1156
 
9.5%
3 142
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4350
53.5%
2 2476
30.5%
1 1156
 
14.2%
3 142
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4350
35.7%
. 4062
33.3%
2 2476
20.3%
1 1156
 
9.5%
3 142
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4350
35.7%
. 4062
33.3%
2 2476
20.3%
1 1156
 
9.5%
3 142
 
1.2%

stalk_color_above_ring
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8306253
Minimum0
Maximum8
Zeros216
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:33.877831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q37
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8934607
Coefficient of variation (CV)0.32474402
Kurtosis2.6289246
Mean5.8306253
Median Absolute Deviation (MAD)0
Skewness-1.868834
Sum23684
Variance3.5851934
MonotonicityNot monotonic
2023-03-04T22:07:33.956460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 2242
55.2%
6 951
23.4%
3 288
 
7.1%
0 216
 
5.3%
4 203
 
5.0%
5 98
 
2.4%
2 47
 
1.2%
1 16
 
0.4%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 216
 
5.3%
1 16
 
0.4%
2 47
 
1.2%
3 288
 
7.1%
4 203
 
5.0%
5 98
 
2.4%
6 951
23.4%
7 2242
55.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 2242
55.2%
6 951
23.4%
5 98
 
2.4%
4 203
 
5.0%
3 288
 
7.1%
2 47
 
1.2%
1 16
 
0.4%
0 216
 
5.3%

stalk_color_below_ring
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8131462
Minimum0
Maximum8
Zeros223
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:34.039326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q37
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9053688
Coefficient of variation (CV)0.32776895
Kurtosis2.5564173
Mean5.8131462
Median Absolute Deviation (MAD)0
Skewness-1.8468988
Sum23613
Variance3.6304304
MonotonicityNot monotonic
2023-03-04T22:07:34.117593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 2212
54.5%
6 953
23.5%
3 271
 
6.7%
4 236
 
5.8%
0 223
 
5.5%
5 98
 
2.4%
2 46
 
1.1%
1 16
 
0.4%
8 7
 
0.2%
ValueCountFrequency (%)
0 223
 
5.5%
1 16
 
0.4%
2 46
 
1.1%
3 271
 
6.7%
4 236
 
5.8%
5 98
 
2.4%
6 953
23.5%
7 2212
54.5%
8 7
 
0.2%
ValueCountFrequency (%)
8 7
 
0.2%
7 2212
54.5%
6 953
23.5%
5 98
 
2.4%
4 236
 
5.8%
3 271
 
6.7%
2 46
 
1.1%
1 16
 
0.4%
0 223
 
5.5%

vein_type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
0.0
4062 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4062
100.0%

Length

2023-03-04T22:07:34.202787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:34.287242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4062
100.0%

Most occurring characters

ValueCountFrequency (%)
0 8124
66.7%
. 4062
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8124
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8124
66.7%
. 4062
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8124
66.7%
. 4062
33.3%

vein_color
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
2.0
3963 
0.0
 
53
1.0
 
45
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 3963
97.6%
0.0 53
 
1.3%
1.0 45
 
1.1%
3.0 1
 
< 0.1%

Length

2023-03-04T22:07:34.356340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:34.448262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3963
97.6%
0.0 53
 
1.3%
1.0 45
 
1.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4115
33.8%
. 4062
33.3%
2 3963
32.5%
1 45
 
0.4%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4115
50.7%
2 3963
48.8%
1 45
 
0.6%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4115
33.8%
. 4062
33.3%
2 3963
32.5%
1 45
 
0.4%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4115
33.8%
. 4062
33.3%
2 3963
32.5%
1 45
 
0.4%
3 1
 
< 0.1%

ring_number
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
1.0
3748 
2.0
 
298
0.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3748
92.3%
2.0 298
 
7.3%
0.0 16
 
0.4%

Length

2023-03-04T22:07:34.525604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:34.613486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3748
92.3%
2.0 298
 
7.3%
0.0 16
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 4078
33.5%
. 4062
33.3%
1 3748
30.8%
2 298
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4078
50.2%
1 3748
46.1%
2 298
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4078
33.5%
. 4062
33.3%
1 3748
30.8%
2 298
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4078
33.5%
. 4062
33.3%
1 3748
30.8%
2 298
 
2.4%

ring_type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
4.0
1960 
0.0
1426 
2.0
628 
1.0
 
32
3.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12186
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 1960
48.3%
0.0 1426
35.1%
2.0 628
 
15.5%
1.0 32
 
0.8%
3.0 16
 
0.4%

Length

2023-03-04T22:07:34.689975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T22:07:34.951317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1960
48.3%
0.0 1426
35.1%
2.0 628
 
15.5%
1.0 32
 
0.8%
3.0 16
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 5488
45.0%
. 4062
33.3%
4 1960
 
16.1%
2 628
 
5.2%
1 32
 
0.3%
3 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
66.7%
Other Punctuation 4062
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5488
67.6%
4 1960
 
24.1%
2 628
 
7.7%
1 32
 
0.4%
3 16
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 4062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5488
45.0%
. 4062
33.3%
4 1960
 
16.1%
2 628
 
5.2%
1 32
 
0.3%
3 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5488
45.0%
. 4062
33.3%
4 1960
 
16.1%
2 628
 
5.2%
1 32
 
0.3%
3 16
 
0.1%

spore_print_color
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6415559
Minimum0
Maximum8
Zeros20
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:35.035983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q37
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.3954168
Coefficient of variation (CV)0.65780036
Kurtosis-1.3835012
Mean3.6415559
Median Absolute Deviation (MAD)2
Skewness0.52038316
Sum14792
Variance5.7380215
MonotonicityNot monotonic
2023-03-04T22:07:35.118150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 1223
30.1%
3 959
23.6%
2 949
23.4%
1 795
19.6%
5 39
 
1.0%
8 29
 
0.7%
4 27
 
0.7%
6 21
 
0.5%
0 20
 
0.5%
ValueCountFrequency (%)
0 20
 
0.5%
1 795
19.6%
2 949
23.4%
3 959
23.6%
4 27
 
0.7%
5 39
 
1.0%
6 21
 
0.5%
7 1223
30.1%
8 29
 
0.7%
ValueCountFrequency (%)
8 29
 
0.7%
7 1223
30.1%
6 21
 
0.5%
5 39
 
1.0%
4 27
 
0.7%
3 959
23.6%
2 949
23.4%
1 795
19.6%
0 20
 
0.5%

population
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.636386
Minimum0
Maximum5
Zeros193
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:35.201704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2478933
Coefficient of variation (CV)0.34316854
Kurtosis1.6940147
Mean3.636386
Median Absolute Deviation (MAD)0
Skewness-1.4178112
Sum14771
Variance1.5572377
MonotonicityNot monotonic
2023-03-04T22:07:35.277315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 2054
50.6%
5 827
20.4%
3 607
 
14.9%
2 218
 
5.4%
0 193
 
4.8%
1 163
 
4.0%
ValueCountFrequency (%)
0 193
 
4.8%
1 163
 
4.0%
2 218
 
5.4%
3 607
 
14.9%
4 2054
50.6%
5 827
20.4%
ValueCountFrequency (%)
5 827
20.4%
4 2054
50.6%
3 607
 
14.9%
2 218
 
5.4%
1 163
 
4.0%
0 193
 
4.8%

habitat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5315116
Minimum0
Maximum6
Zeros1566
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size63.5 KiB
2023-03-04T22:07:35.353739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7285261
Coefficient of variation (CV)1.1286406
Kurtosis-0.34707171
Mean1.5315116
Median Absolute Deviation (MAD)1
Skewness0.94815319
Sum6221
Variance2.9878026
MonotonicityNot monotonic
2023-03-04T22:07:35.428998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1566
38.6%
1 1047
25.8%
4 588
 
14.5%
2 421
 
10.4%
5 189
 
4.7%
3 157
 
3.9%
6 94
 
2.3%
ValueCountFrequency (%)
0 1566
38.6%
1 1047
25.8%
2 421
 
10.4%
3 157
 
3.9%
4 588
 
14.5%
5 189
 
4.7%
6 94
 
2.3%
ValueCountFrequency (%)
6 94
 
2.3%
5 189
 
4.7%
4 588
 
14.5%
3 157
 
3.9%
2 421
 
10.4%
1 1047
25.8%
0 1566
38.6%

edible
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
False
2104 
True
1958 
ValueCountFrequency (%)
False 2104
51.8%
True 1958
48.2%
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Interactions

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Correlations

2023-03-04T22:07:35.619720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
cap_shapecap_colorodorgill_colorstalk_color_above_ringstalk_color_below_ringspore_print_colorpopulationhabitatcap_surfacebruisesgill_attachmentgill_spacinggill_sizestalk_shapestalk_rootstalk_surface_above_ringstalk_surface_below_ringvein_colorring_numberring_typeedible
cap_shape1.000-0.043-0.005-0.014-0.0000.008-0.0220.051-0.0550.4360.2670.1530.0640.3520.3080.3620.1080.1110.0920.1900.2110.253
cap_color-0.0431.000-0.2850.1130.1190.095-0.250-0.1880.1310.2260.2210.2630.3950.4930.6000.3960.2480.3330.1400.4130.5850.226
odor-0.005-0.2851.000-0.1300.1110.1120.497-0.038-0.0250.2420.6640.2350.4270.7610.5790.6230.4140.5050.0950.7420.6960.971
gill_color-0.0140.113-0.1301.0000.000-0.014-0.344-0.002-0.2200.2540.5820.5740.3270.7620.5680.5120.3330.3560.3400.2670.4940.657
stalk_color_above_ring-0.0000.1190.1110.0001.0000.5770.249-0.4140.1560.2480.4800.9800.3870.3660.5290.3630.4370.4330.8160.7690.6710.525
stalk_color_below_ring0.0080.0950.112-0.0140.5771.0000.231-0.4110.1410.2500.4830.9800.3640.3430.5440.3640.3940.4360.6160.7690.6620.510
spore_print_color-0.022-0.2500.497-0.3440.2490.2311.000-0.2120.1160.2370.6180.8490.3000.6680.4660.5190.4120.4200.5160.3660.5530.751
population0.051-0.188-0.038-0.002-0.414-0.411-0.2121.000-0.2770.2590.3020.4140.6830.5120.4660.5360.3230.3020.2200.4140.3390.496
habitat-0.0550.131-0.025-0.2200.1560.1410.116-0.2771.0000.2490.5300.4420.5640.4970.4220.5420.3020.2990.2660.4270.3000.446
cap_surface0.4360.2260.2420.2540.2480.2500.2370.2590.2491.0000.1250.2140.3390.2800.0750.3190.1430.1610.1310.1010.2250.213
bruises0.2670.2210.6640.5820.4800.4830.6180.3020.5300.1251.0000.1340.3000.3860.0700.5870.5660.5590.1290.0700.7720.503
gill_attachment0.1530.2630.2350.5740.9800.9800.8490.4140.4420.2140.1341.0000.0660.1080.1850.2170.0960.1180.9650.1670.2240.133
gill_spacing0.0640.3950.4270.3270.3870.3640.3000.6830.5640.3390.3000.0661.0000.1310.0860.5930.4530.3830.0720.2420.2880.353
gill_size0.3520.4930.7610.7620.3660.3430.6680.5120.4970.2800.3860.1080.1311.0000.2270.6460.1810.1490.1060.1960.5840.551
stalk_shape0.3080.6000.5790.5680.5290.5440.4660.4660.4220.0750.0700.1850.0860.2271.0000.3960.3010.3490.1800.3320.6210.073
stalk_root0.3620.3960.6230.5120.3630.3640.5190.5360.5420.3190.5870.2170.5930.6460.3961.0000.3130.5590.1340.2580.4440.423
stalk_surface_above_ring0.1080.2480.4140.3330.4370.3940.4120.3230.3020.1430.5660.0960.4530.1810.3010.3131.0000.4630.2020.1380.4520.591
stalk_surface_below_ring0.1110.3330.5050.3560.4330.4360.4200.3020.2990.1610.5590.1180.3830.1490.3490.5590.4631.0000.0830.2400.5140.573
vein_color0.0920.1400.0950.3400.8160.6160.5160.2200.2660.1310.1290.9650.0720.1060.1800.1340.2020.0831.0000.0180.0890.150
ring_number0.1900.4130.7420.2670.7690.7690.3660.4140.4270.1010.0700.1670.2420.1960.3320.2580.1380.2400.0181.0000.7140.206
ring_type0.2110.5850.6960.4940.6710.6620.5530.3390.3000.2250.7720.2240.2880.5840.6210.4440.4520.5140.0890.7141.0000.606
edible0.2530.2260.9710.6570.5250.5100.7510.4960.4460.2130.5030.1330.3530.5510.0730.4230.5910.5730.1500.2060.6061.000

Missing values

2023-03-04T22:07:31.271360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-04T22:07:31.555698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cap_shapecap_surfacecap_colorbruisesodorgill_attachmentgill_spacinggill_sizegill_colorstalk_shapestalk_rootstalk_surface_above_ringstalk_surface_below_ringstalk_color_above_ringstalk_color_below_ringvein_typevein_colorring_numberring_typespore_print_colorpopulationhabitatedible
66075.02.04.00.07.01.00.01.00.01.00.02.01.07.07.00.02.01.00.07.04.00.0True
52623.03.04.00.05.01.00.01.010.00.00.01.03.07.08.00.02.01.00.07.04.00.0True
75273.03.04.00.07.01.00.01.00.01.00.01.02.06.07.00.02.01.00.07.04.00.0True
33092.03.04.01.05.01.00.00.09.01.01.02.02.06.06.00.02.01.04.03.05.00.0False
21235.00.04.01.05.01.00.00.09.01.01.02.02.07.06.00.02.01.04.02.04.00.0False
17825.02.08.00.05.01.01.00.04.01.03.00.02.07.07.00.02.01.00.02.00.01.0False
41825.03.09.00.02.01.00.00.02.00.01.01.01.06.04.00.02.01.02.01.05.00.0True
23345.03.02.01.05.01.00.00.09.01.01.02.02.06.03.00.02.01.04.03.05.00.0False
16142.02.04.01.06.01.00.01.07.00.03.02.02.07.07.00.02.01.04.03.04.01.0True
57392.02.00.01.02.01.00.00.010.01.01.02.00.07.07.00.02.01.04.01.03.05.0True
cap_shapecap_surfacecap_colorbruisesodorgill_attachmentgill_spacinggill_sizegill_colorstalk_shapestalk_rootstalk_surface_above_ringstalk_surface_below_ringstalk_color_above_ringstalk_color_below_ringvein_typevein_colorring_numberring_typespore_print_colorpopulationhabitatedible
23745.03.04.01.05.01.00.00.07.01.01.02.02.07.03.00.02.01.04.03.05.00.0False
78292.02.04.00.05.00.00.00.06.00.00.02.02.05.05.00.00.01.04.03.04.02.0False
55870.03.05.01.05.01.00.00.010.00.01.02.02.07.07.00.02.02.04.05.04.03.0True
76490.02.08.00.05.01.01.00.07.00.00.01.02.07.07.00.02.02.04.07.03.01.0False
4455.03.04.01.03.01.00.00.07.00.04.02.03.07.07.00.02.01.04.02.03.01.0False
48745.03.09.00.02.01.00.00.03.00.01.01.01.06.06.00.02.01.02.01.04.04.0True
77775.02.08.00.05.01.01.00.02.00.00.01.01.07.07.00.02.02.04.07.03.01.0False
80400.02.04.00.05.00.00.00.05.00.00.02.02.05.05.00.01.01.04.08.04.02.0False
55962.02.00.01.05.01.00.00.01.00.00.02.02.02.07.00.02.02.00.07.01.06.0False
76283.02.04.00.07.01.00.01.00.01.00.01.02.07.07.00.02.01.00.07.04.04.0True